Готові списки джерел за темами / Particle Swarm Optimization [PSO]; Maximum Power Point Tracking [MPPT]; Photovoltaic [PV] System

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Добірка наукової літератури з теми "Particle Swarm Optimization [PSO]; Maximum Power Point Tracking [MPPT]; Photovoltaic [PV] System"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Particle Swarm Optimization [PSO]; Maximum Power Point Tracking [MPPT]; Photovoltaic [PV] System"

Mukti, Ersalina Werda, Agus Risdiyanto, Ant Ardath Kristi, and Rudi Darussalam. "Particle Swarm Optimization (PSO) based Photovoltaic MPPT Algorithm under the Partial Shading Condition." Jurnal Elektronika dan Telekomunikasi 23, no. 2 (2023): 99. http://dx.doi.org/10.55981/jet.552.

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Анотація:

Performance of solar photovoltaic (PV) system degrades considerably when the surface of the PV array is covered with shadows indicated by the reduction in the generated power. Partial shading condition causes the PV array to produce multiple peaks in PV’s characteristic curve causing the non-optimal power generation of the conventional maximum power point (MPPT) algorithm. Therefore, we proposed an optimization of PV system through the design of particle swarm optimization (PSO) algorithm in the MPPT in order to enhance the power extraction during shaded conditions. The evaluation of the perfo

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Sharma, Abhishek, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput, and Brian Azzopardi. "A Novel TSA-PSO based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions." Energies 15, no. 9 (2022): 3164. https://doi.org/10.3390/en15093164.

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In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algor

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Duranay, Zeynep Bala, and Hanifi Güldemir. "Mitigating Partial Shading Effects in Photovoltaic Systems Using Particle Swarm Optimization-Tuned Sliding Mode Control." Processes 13, no. 5 (2025): 1463. https://doi.org/10.3390/pr13051463.

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The power output of a photovoltaic (PV) system is inherently dependent on climatic factors. To maximize the energy harvested from PV arrays, maximum power point tracking (MPPT) algorithms are employed. These algorithms dynamically adjust the operating point of the system to extract the maximum available power. However, under partial shading conditions (PSCs), conventional MPPT algorithms often fail to locate the global maximum power point, leading to suboptimal power extraction. In this study, a robust MPPT technique based on sliding mode control (SMC) is proposed to enhance tracking efficienc

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Iman, M. I., M. F. Roslan, Pin Jern Ker, and M. A. Hannan. "An Intelligent Maximum Power Point Tracking Algorithm for Photovoltaic System." International Journal of Engineering & Technology 7, no. 4.35 (2018): 457. http://dx.doi.org/10.14419/ijet.v7i4.35.22861.

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This work comprehensively demonstrates the performance analysis of Fuzzy Logic Controller (FLC) with Particle Swarm Optimization (PSO) Maximum Power Point Tracker (MPPT) algorithm on a stand-alone Photovoltaic (PV) applications systems. A PV panel, DC-DC Boost converter and resistive load was utilized as PV system. Three different MPPT algorithms were implemented in the converter. The result obtained from the converter was analyzed and compared to find the best algorithm to be used to identify the point in which maximum power can be achieve in a PV system. The objective is to reduce the time t

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Priya, Murugesan Vishnu, and Gopal Anandha Kumar. "Particle swarm optimization for enhanced maximum power point tracking: design and implementation in Proteus." International Journal of Power Electronics and Drive Systems (IJPEDS) 15, no. 1 (2024): 491. http://dx.doi.org/10.11591/ijpeds.v15.i1.pp491-497.

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This study introduces a photovoltaic (PV) system model tailored for PV design, incorporating a particle swarm optimization (PSO) MPPT technique to achieve optimal efficiency, swift responsiveness, and cost-effectiveness. To initiate, a PV module model is formulated within Proteus using SPICE coding. Subsequently, an experimental test setup is deployed to authenticate and validate the model. Following this, a PSO-based MPPT algorithm is proposed, which overcomes the limitations of conventional perturb and observe (P&amp;O) and incremental conductance MPPT methods, notably reducing the relia

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Evi, Nafiatus Sholikhah, Ayub Windarko Novie, and Sumantri Bambang. "Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4559–70. https://doi.org/10.11591/ijece.v12i5.pp4559-4570.

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Анотація:

A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is compared with p

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Aji, Akbar Firdaus, Tri Yunardi Riky, Inaiyah Agustin Eva, D. N. Nahdliyah Sisca, and Aryo Nugroho Teguh. "An improved control for MPPT based on FL-PSo to minimize oscillation in photovoltaic system." International Journal of Power Electronics and Drive System (IJPEDS) 11, no. 2 (2020): 1082–87. https://doi.org/10.11591/ijpeds.v11.i2.pp1082-1087.

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Анотація:

Photovoltaic (PV) is a source of electrical energy derived from solar energy and has a poor level of efficiency. This efficiency is influenced by PV condition, weather, and equipments like Maximum Power Point Tracking (MPPT). MPPT control is widely used to improve PV efficiency because MPPT can produce optimal power in various weather conditions. In this paper, MPPT control is performed using the Fuzzy Logic-Particle Swarm Optimization (FL-PSO) method. This FL-PSO is used to get the Maximum Power Point (MPP) and minimize the output power oscillation from PV. From the simulation results using F

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Sharma, Abhishek, Abhinav Sharma, Vibhu Jately, Moshe Averbukh, Shailendra Rajput, and Brian Azzopardi. "A Novel TSA-PSO Based Hybrid Algorithm for GMPP Tracking under Partial Shading Conditions." Energies 15, no. 9 (2022): 3164. http://dx.doi.org/10.3390/en15093164.

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Sholikhah, Evi Nafiatus, Novie Ayub Windarko, and Bambang Sumantri. "Tunicate swarm algorithm based maximum power point tracking for photovoltaic system under non-uniform irradiation." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 5 (2022): 4559. http://dx.doi.org/10.11591/ijece.v12i5.pp4559-4570.

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Анотація:

A new maximum power point tracking (MPPT) technique based on the bio-inspired metaheuristic algorithm for photovoltaic system (PV system) is proposed, namely tunicate swarm algorithm-based MPPT (TSA-MPPT). The proposed algorithm is implemented on the PV system with five PV modules arranged in series and integrated with DC-DC buck converter. Then, the PV system is tested in a simulation using PowerSim (PSIM) software. TSA-MPPT is tested under varying irradiation conditions both uniform irradiation and non-uniform irradiation. Furthermore, to evaluate the performance, TSA-MPPT is com

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Більше джерел

Дисертації з теми "Particle Swarm Optimization [PSO]; Maximum Power Point Tracking [MPPT]; Photovoltaic [PV] System"

Chellal, Majd. "Experimental evaluation of Kalman filter based MPPT in grid-connected PV system." Master's thesis, 2022. http://hdl.handle.net/10198/25144.

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Анотація:

Mestrado de dupla diplomação com a Ecóle Supérieur en Sciences Apliquées Photovoltaic (PV) energy is becoming an important alternative energy source, since it is abundant in nature, non-polluting and requires low maintenance. However, it suffers from low energy conversion efficiency, which can be even lower if the photovoltaic generator does not operate around a so-called Maximum Power Point (MPP). Tracking this point, which changes its location depending on weather conditions, is a very important step in the design of a photovoltaic system. Several techniques have been investigated in

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Chen, Wei-Ju, and 陳威儒. "Multi-Cluster-Based Particle Swarm Optimization (PSO) Algorithm for Photovoltaic (PV) Maximum Power Point Tracking." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/50460957173204975893.

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碩士 國立彰化師範大學 電機工程學系 102 In this thesis, a multi-cluster-based particle swarm optimization (PSO) algorithm for photovoltaic (PV) maximum power point tracking (MPPT) is proposed to promote the MPPT performance in partial shading condition. During the tracking process, each PV module is viewd as a particle and the PV modules which have similar characteristic are put into the same cluster.The particles in the same cluster can refer the data to each other to realize to track the global maximum power point (GMPP) of the P-V curve of PV modules, it can overcome the problem of that the conv

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Частини книг з теми "Particle Swarm Optimization [PSO]; Maximum Power Point Tracking [MPPT]; Photovoltaic [PV] System"

Ghosh, Arnab, Annesha Sahu, and Puja Barua. "Particle Swarm Optimization-Based MPPT for PV Applications and Its Comparison With Incremental Conductance Method." In Optimization Techniques for Hybrid Power Systems. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0492-1.ch013.

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The maximum power point tracking (MPPT) method for photovoltaic (PV) applications using incremental conductance (INC) and particle swarm optimization (PSO) is compared in this work. The chapter contains a comprehensive investigation of the underlying ideas and practical elements of the INC and PSO MPPT methodologies. The MPPT control system's PV module, DC-DC boost converter, and INC and PSO based MPPT algorithms are all simulated using MATLAB Simulink. A comparison of the system's performance under various solar irradiation rates—both fast and slow—is also made in the simulation. The simulati

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Wang, Ning, and Weiguang Dong. "Improve DBO Optimization Algorithm to Realize the Maximum Power Point Tracking of the Photovoltaic System." In Advances in Transdisciplinary Engineering. IOS Press, 2024. http://dx.doi.org/10.3233/atde240482.

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Анотація:

To improve the conversion efficiency of the photovoltaic power generation system, a proposed method of tracking the maximum power point of the photovoltaic array based on the improved mantis algorithm is introduced due to the local shading of the photovoltaic array presenting multiple peak characteristics in the P-U characteristic curve. The tracking efficiency of the traditional maximum power point tracking (MPPT) algorithm is observed to be low under these circumstances. The superiority of the improved Mantis (IMSDBO) algorithm over the standard dung beetle algorithm (DBO), sparrow search al

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S., Socrates, K. K. Manivannan, A. Krishnakumari, M. Saravanan, M. Shanmugapriya, and M. D. Raj Kamal. "Enhancing Photovoltaic System Performance Using PSO for Maximum Power Point Tracking and DC-Bus Voltage Regulation in Grid-Connected PV Systems." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3314-3.ch017.

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Анотація:

This research presents an integrated approach to enhance the performance of grid-connected photovoltaic (PV) systems by combining sensor-based orientation with the practical swarm optimization (PSO) algorithm for maximum power point tracking (MPPT) and a proportional-integral (PI) controller for DC voltage regulation. Solar positioning and infrared sensors provide real-time data, guiding the dynamic movement of the solar panel. The PSO algorithm optimizes motor movements for efficient MPPT, ensuring the panel aligns with the optimal sun position throughout the day. Simultaneously, the PI contr

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Тези доповідей конференцій з теми "Particle Swarm Optimization [PSO]; Maximum Power Point Tracking [MPPT]; Photovoltaic [PV] System"

Khazain, M. A., N. M. Hidayat, K. Burhanudin, and E. Abdullah. "Boost Converter of Maximum Power Point Tracking (MPPT) Using Particle Swarm Optimization (PSO) Method." In 2021 IEEE 12th Control and System Graduate Research Colloquium (ICSGRC). IEEE, 2021. http://dx.doi.org/10.1109/icsgrc53186.2021.9515228.

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Beltran, Alfredo S., and Sandip Das. "Particle Swarm Optimization with Reducing Boundaries (PSO-RB) for Maximum Power Point Tracking of Partially Shaded PV Arrays." In 2020 IEEE 47th Photovoltaic Specialists Conference (PVSC). IEEE, 2020. http://dx.doi.org/10.1109/pvsc45281.2020.9300516.

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Zafar, Muhammad Hamza, Majad Mansoor, Noman Mujeeb Khan, and Filippo Sanfilippo. "Optimal Tuning of PID Controller for Boost Converter using Meta-Heuristic Algorithm for Renewable Energy Applications." In International Conference on Mechanical, Automotive and Mechatronics Engineering. ECER, 2023. http://dx.doi.org/10.53375/icmame.2023.32.

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Анотація:

The Dynamic Levy Flight Chimp optimisation (DLFC) method is used in this study to optimise the Proportional- Integral-Derivative (PID) Controller for the Boost converter. As a possible application, the tuned PID controller is utilised to adjust voltages in the use of renewable power sources. The maximum power point tracking control approach based on machine learning (ML) is used to anticipate the reference voltages for the solar system based on the irradiance and the ambient temperature. The tuned PID controller uses this reference signal to regulate the maximum power point (MPP) voltages. To

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